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Asset logo for symbol CARR
Carrier Global
Asset logo for symbol CARR



AI Deep-Learning Forecast (CARR)

Analyst Forecast

Period: Next 12 Months

Analyst Ratings

Carrier Global (CARR) Stocklytics Forecast

The stock price prediction for Carrier Global Corp (CARR) remains uncertain in the upcoming years. However, several analysts and AI algorithms have provided their forecasts and price targets for CARR. Based on their analysis, CARR is expected to experience a steady growth trajectory. According to machine learning prediction models, CARR has the potential to reach new highs in the future. These models take into account various factors, such as historical trends, market sentiment, and financial indicators, to forecast the stock's performance.

In terms of price targets, analysts predict that CARR will achieve significant gains by 2023. The estimated price target for CARR by the end of 2023 is projected to be well above its current trading level. This suggests that CARR may be a good investment opportunity for those considering a long-term investment strategy. However, it is important to note that these projections are based on historical data and market conditions, which can always shift unexpectedly.

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Frequently Asked Questions

What is the analyst price prediction for Carrier Global (CARR) stock?

Analysts have set a target price of $53.57 for Carrier Global (CARR), based on forecasts from 23 analysts. The predicted price range extends from a high of $63 to a low of $45. This represents a potential increase of up to -6.54% and a decrease of -33.24% from the current price of $67.41. These forecasts are as of 2023 Apr 13.

What are the analyst ratings for Carrier Global (CARR) stock?

Currently, there are no analyst ratings available for Carrier Global (CARR), possibly due to insufficient coverage or recent updates.

What is the AI price prediction for Carrier Global (CARR) stock?

At present, there is no AI or machine-learning-based price prediction available for Carrier Global (CARR) stock. The lack of a forecast could stem from various factors, such as inadequate data or the necessity for model recalibration.


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